CAFE-DETR: A Sesame Plant and Weed Classification and Detection Algorithm Based on Context-Aware Feature Enhancement

Weed competition represents a primary constraint in sesame production, causing substantial yield losses typically ranging from 18 to 68% under inadequate control measures. Precise crop–weed discrimination remains challenging due to morphological similarities, complex field conditions, and vegetation overlapping. To address these issues, we developed Context-Aware Feature-Enhanced Detection Transformer (CAFE-DETR), an enhanced Real-Time Detection Transformer (RT-DETR) architecture optimized for sesame–weed identification. First, the C2f with a Unified Attention-Gating (C2f-UAG) module integrates unified head attention with convolutional gating mechanisms to enhance morphological discrimination capabilities. Second, the Hierarchical Context-Adaptive Fusion Network (HCAF-Net) incorporates hierarchical context extraction and spatial–channel enhancement to achieve multi-scale feature representation. Furthermore, the Polarized Linear Spatial Multi-scale Fusion Network (PLSM-Encoder) reduces computational complexity from O(N2) to O(N) through polarized linear attention while maintaining global semantic modeling. Additionally, the Focaler-MPDIoU loss function improves localization accuracy through point distance constraints and adaptive sample focusing. Experimental results on the sesame–weed dataset demonstrate that CAFE-DETR achieves 90.0% precision, 89.5% mAP50, and 59.5% mAP50–95, representing improvements of 13.07%, 4.92%, and 2.06% above the baseline RT-DETR, respectively, while reducing computational cost by 23.73% (43.4 GFLOPs) and parameter count by 10.55% (17.8 M). These results suggest that CAFE-DETR is a viable alternative for implementation in intelligent spraying systems and precision agriculture platforms. Notably, this study lacks external validation, cross-dataset testing, and field trials, which limits the generalizability of the model to diverse real-world agricultural scenarios.